PIK3IP1 Antibody

What is PIK3IP1 and what are its structural characteristics?

PIK3IP1 is a transmembrane protein that functions as a negative regulator of PI3K signaling. The protein contains multiple domains including a kringle domain in its extracellular region and a cytoplasmic domain that interacts with PI3K. It is approximately 37 kDa in size and undergoes post-translational modifications including O-glycosylation at S39, N-glycosylation at N66, and ubiquitination at K198 . The protein sequence contains characteristic motifs that enable it to act as a membrane-spanning regulator, with its transmembrane region (amino acids approximately 170-190) connecting its extracellular and intracellular functional domains .

How does PIK3IP1 regulate immune responses?

PIK3IP1 serves as an essential rheostat for T-cell-mediated immunity. Mechanistically, PIK3IP1 inhibits T-cell receptor (TCR) signaling by mediating the degradation of SLP76 through oligomerization via its extracellular region . This negative regulatory function is critical for maintaining immune homeostasis. In PIK3IP1-deficient mice, enhanced T-cell responsiveness is observed, particularly upon immunization with neoantigens, demonstrating PIK3IP1's role in controlling T-cell activation thresholds . Unlike other negative immune regulators (such as PD-1, CTLA-4, and VISTA) which are upregulated in autoimmune conditions, PIK3IP1 shows decreased expression in autoimmune settings, suggesting a unique regulatory pattern .

What is the relationship between PIK3IP1 and cellular metabolism?

Recent research reveals that PIK3IP1 plays a critical role in metabolic regulation, particularly through the PIK3IP1/HIF1α/glycolysis axis. PIK3IP1 regulates cellular metabolism of pathogenic inflammatory T cells, inhibiting the development of autoimmune diseases . The reduction of PIK3IP1 expression is associated with a shift from oxidative phosphorylation toward glycolysis in T cells, promoting inflammatory phenotypes. This metabolic switch is fundamental to T-cell activation and pathogenicity in autoimmune conditions. The PIK3IP1/HIF1α/glycolysis axis has been identified as a potential therapeutic target for autoimmune disease treatment .

How is PIK3IP1 expression altered in autoimmune diseases?

PIK3IP1 expression is significantly downregulated in multiple autoimmune diseases. Flow cytometric analysis of peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and multiple sclerosis (MS) shows a marked reduction in the proportion of PIK3IP1-positive T cells compared to healthy donors . This pattern has been validated through bulk RNA sequencing data, which confirms decreased PIK3IP1 expression in SLE, RA, MS, and Sjögren's syndrome (SS) . Importantly, reduced PIK3IP1 expression correlates with disease progression and severity, with greater reductions observed as disease activity increases .

What cytokine mechanisms regulate PIK3IP1 expression in autoimmune conditions?

IL-21 appears to be a key driver of PIK3IP1 downregulation during autoimmunity. In patients with SLE, RA, and MS, PIK3IP1 expression decreases as IL-21 production in T cells increases . In vitro experiments demonstrate that while IL-2 or IFN-α stimulation alone has no effect on PIK3IP1 expression in T cells, IL-21 stimulation results in notable reduction of PIK3IP1 expression, similar to effects observed with anti-CD3/CD28 stimulation . Treatment with tofacitinib, an IL-21 signaling inhibitor, leads to recovery of PIK3IP1 expression, confirming the regulatory relationship between IL-21 signaling and PIK3IP1 expression .

How does PIK3IP1 expression affect T-cell inflammatory profiles?

Reduced PIK3IP1 expression on T cells is associated with increased production of proinflammatory cytokines, including IL-17, TNF-α, IFN-γ, and IL-2 . Single-cell RNA sequencing analysis reveals that PIK3IP1 clusters with quiescence-associated genes, suggesting its role in maintaining T cells in a non-inflammatory state . In cancer immunology research, PIK3IP1 expression has been found to correlate with T-cell dysfunction in human tumors, further supporting its role as a negative regulator of T-cell activation and effector functions .

What are the validated applications for PIK3IP1 antibodies?

PIK3IP1 antibodies have been validated for several applications including Western blot (WB), immunohistochemistry (IHC), and immunofluorescence/immunocytochemistry (IF/ICC) . These applications enable researchers to detect and quantify PIK3IP1 protein in denatured protein samples, tissue sections (both paraffin-embedded and frozen), and cellular preparations. PIK3IP1 antibodies have been effectively used to investigate PIK3IP1 expression patterns in various immune cell subsets, particularly T cells, in both health and disease states .

How should samples be prepared for optimal PIK3IP1 detection?

For flow cytometric analysis of PIK3IP1 expression in PBMCs, samples should be processed following standard isolation protocols and stained with appropriate antibody panels including PIK3IP1-specific antibodies . For Western blot applications, protein extraction should be performed using buffers that effectively solubilize membrane proteins, as PIK3IP1 is a transmembrane protein. Post-translational modifications (PTMs) should be considered when analyzing PIK3IP1, as it undergoes O-glycosylation, N-glycosylation, and ubiquitination, which may affect antibody binding or protein migration patterns .

What controls should be included when using PIK3IP1 antibodies?

Appropriate controls are essential for validating PIK3IP1 antibody specificity. Positive controls should include samples known to express PIK3IP1, such as T cells from healthy donors . Negative controls might include samples from PIK3IP1 knockout models or cells treated with siRNA against PIK3IP1. For flow cytometry applications, fluorescence minus one (FMO) controls should be included to accurately set gates for PIK3IP1-positive populations. When analyzing PIK3IP1 expression in disease states, comparing multiple sample types (e.g., matched patient and healthy donor samples) is recommended for meaningful interpretation .

How can PIK3IP1 antibodies be used to investigate T-cell metabolism?

PIK3IP1 antibodies can be employed to study the relationship between PIK3IP1 expression and T-cell metabolic profiles. By combining PIK3IP1 staining with metabolic assays (such as Seahorse analysis for measuring oxygen consumption and extracellular acidification rates), researchers can correlate PIK3IP1 levels with metabolic parameters . Co-staining with markers of glycolysis (such as GLUT1 or HK2) or with HIF1α can help elucidate the PIK3IP1/HIF1α/glycolysis axis in different T-cell subsets and disease states . This approach can reveal how PIK3IP1 expression influences the metabolic switch from oxidative phosphorylation to glycolysis during T-cell activation and in autoimmune conditions.

What experimental designs are optimal for studying PIK3IP1's role in autoimmunity?

To investigate PIK3IP1's role in autoimmunity, several experimental approaches can be employed:

• Longitudinal studies tracking PIK3IP1 expression in patients with varying disease severities and during disease flares/remissions

• In vitro T-cell stimulation models using cytokines relevant to autoimmunity (particularly IL-21)

• Genetic manipulation studies using PIK3IP1 knockout or overexpression systems

• Animal models of autoimmunity comparing wild-type and PIK3IP1-deficient animals

These approaches should include comprehensive assessment of T-cell function, inflammatory cytokine production, and metabolic parameters . Integration of multiple readouts (protein expression, functional assays, and transcriptomic analyses) provides the most complete understanding of PIK3IP1's role in disease pathogenesis.

How can PIK3IP1 antibodies be used to identify potential therapeutic targets?

PIK3IP1 antibodies can help identify and validate therapeutic targets within the PIK3IP1/HIF1α/glycolysis axis. By characterizing PIK3IP1 expression patterns and associated signaling pathways in different disease states, researchers can pinpoint specific nodes for therapeutic intervention . Co-immunoprecipitation studies using PIK3IP1 antibodies can identify protein interaction partners that might serve as alternative targets. Additionally, screening compounds that modulate PIK3IP1 expression or function (e.g., IL-21 signaling inhibitors like tofacitinib) can help develop targeted therapies for autoimmune diseases characterized by PIK3IP1 dysregulation .

What are common challenges in PIK3IP1 detection and quantification?

Several technical challenges may arise when working with PIK3IP1 antibodies:

Understanding PIK3IP1's post-translational modifications is particularly important, as these may affect antibody recognition and protein function .

How should contradictory findings in PIK3IP1 research be approached?

When encountering contradictory findings regarding PIK3IP1 expression or function:

• Verify antibody specificity using multiple approaches (different antibody clones, genetic controls)

• Consider context-dependent expression patterns (cell type, activation state, disease context)

• Evaluate methodology differences between studies (sample processing, detection methods)

• Assess timing of measurements (acute vs. chronic conditions, disease progression stages)

• Analyze the impact of different inflammatory environments on PIK3IP1 regulation

The unique expression pattern of PIK3IP1 in autoimmunity (downregulation rather than upregulation as seen with other negative immune regulators) highlights the importance of careful experimental design and interpretation .

What are best practices for quantifying changes in PIK3IP1 expression?

For accurate quantification of PIK3IP1 expression changes:

• Use multiple detection methods when possible (flow cytometry, Western blot, qPCR)

• Include appropriate normalization controls (housekeeping proteins, total protein stains)

• Employ objective quantification methods (digital image analysis for IHC, median fluorescence intensity for flow cytometry)

• Account for potential confounding factors (patient demographics, treatment history)

• Use statistical approaches appropriate for the data distribution and study design

When analyzing patient samples, it's important to correlate PIK3IP1 expression with clinical parameters and other molecular markers to establish meaningful associations with disease mechanisms .